Arbeitsgruppe: Lernen mit Neuronalen Methoden auf Strukturierten Daten

T. Villmann, Relevance Learning for unsupervised and supervised neural network models

We consider neural networks of both types, unsupervised and supervised learning models. In the first case we review extensions of the Self-Organizing Map (SOM) and the Neural Gas (NG) which can be used for learning of relevant informations contained in the data (structure adaptaion, magnification control). This can be taken as a kind of knowledge discovery in data mining. In the latter case of supervised learning we will consider the task to discover what data/structures are relevant to obtain a given classfication. In particular, we focus on determining relevant input dimensions in data according to a given classification what we call relevance learning. We give views to some recent developments in generalization of well known vector quantization algorithms as LVQ and NG.

B.Hammer